We compare our approach to other state-of-the-art techniques that are developed for unsupervised medical image segmentation. To the best of our knowledge, no prior work addresses source-free UDA for semantic segmentation, so we compare our work against existing UDA techniques that need source data for alignment. We compare against adversarial approaches PnP-AdaNet [1]}, SynSeg-Net [2]}, AdaOutput [3]}, CycleGAN [4]}, CyCADA [5]}, SIFA [6]}, ARL-GAN [7]}, DSFN [8]}, SASAN [9]}, DSAN [10]}. These works are recently developed methods for semantic segmentation that should serve as upper bounds for our method, because we do not process the source domain data directly. We reiterate that the advantage of our method is to preserve privacy and we do not claim best performance.
